AVQVC: One-Shot Voice Conversion By Vector Quantization With Applying Contrastive Learning

Framework of AVQVC

Abstract

Voice Conversion(VC) refers to changing the timbre of a speech while retaining the discourse content. Recently, many works have focused on disentangle-based learning techniques to separate the timbre and the linguistic content information from a speech signal. Once successful, voice conversion will be feasible and straightforward. This paper proposed a novel one-shot voice conversion framework based on vector quantization voice conversion (VQVC) and AutoVC, called AVQVC. A new training method is applied to VQVC to separate content and timbre information from speech more effectively. The result shows that this approach has better performance than VQVC in separating content and timbre to improve the sound quality of generated speech.

Type
Publication
In 2022 IEEE International Conference on Acoustics, Speech and Signal Processing
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Huaizhen Tang
Huaizhen Tang
University of Science and Technology of China
Ning Cheng
Ning Cheng
Director